'''simple docstring'''




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCamelCase					:		Union[str, Any]							      =		16
_UpperCamelCase					:		int							      =		32



def     __snake_case			(							lowerCAmelCase							:  Optional[int]   ,	lowerCAmelCase							:  Any = 16      ):
 __UpperCAmelCase								=       AutoTokenizer.from_pretrained('bert-base-cased'      )
 __UpperCAmelCase								=       load_dataset('glue'   ,	'mrpc'      )

 def tokenize_function(lowerCAmelCase							:  Tuple      ):
  # max_length=None => use the model max length (it's actually the default)
  __UpperCAmelCase								=       tokenizer(examples['sentence1']   ,	examples['sentence2']   ,	truncation=lowerCAmelCase   ,	max_length=lowerCAmelCase      )
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 # starting with the main process first:
 with accelerator.main_process_first():
  __UpperCAmelCase								=       datasets.map(
      lowerCAmelCase   ,	batched=lowerCAmelCase   ,	remove_columns=['idx', 'sentence1', 'sentence2']   ,	)

 # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
 # transformers library
 __UpperCAmelCase								=       tokenized_datasets.rename_column('label'   ,	'labels'      )

 def collate_fn(lowerCAmelCase							:  Optional[Any]      ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  __UpperCAmelCase								=       128 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   __UpperCAmelCase								=       16
  elif accelerator.mixed_precision != "no":
   __UpperCAmelCase								=       8
  else:
   __UpperCAmelCase								=       None

  return tokenizer.pad(
      lowerCAmelCase   ,	padding='longest'   ,	max_length=lowerCAmelCase   ,	pad_to_multiple_of=lowerCAmelCase   ,	return_tensors='pt'   ,	)

 # Instantiate dataloaders.
 __UpperCAmelCase								=       DataLoader(
     tokenized_datasets['train']   ,	shuffle=lowerCAmelCase   ,	collate_fn=lowerCAmelCase   ,	batch_size=lowerCAmelCase      )
 __UpperCAmelCase								=       DataLoader(
     tokenized_datasets['validation']   ,	shuffle=lowerCAmelCase   ,	collate_fn=lowerCAmelCase   ,	batch_size=lowerCAmelCase      )

 return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     _UpperCamelCase					:		List[Any]							      =		mocked_dataloaders  # noqa: F811



def     __snake_case			(							lowerCAmelCase							:  Dict   ,	lowerCAmelCase							:  Dict      ):



 if os.environ.get('TESTING_MOCKED_DATALOADERS'   ,	lowerCAmelCase      ) == "1":
  __UpperCAmelCase								=       2
 # Initialize accelerator
 __UpperCAmelCase								=       Accelerator(cpu=args.cpu   ,	mixed_precision=args.mixed_precision      )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __UpperCAmelCase								=       config['lr']
 __UpperCAmelCase								=       int(config['num_epochs']      )
 __UpperCAmelCase								=       int(config['seed']      )
 __UpperCAmelCase								=       int(config['batch_size']      )

 __UpperCAmelCase								=       evaluate.load('glue'   ,	'mrpc'      )

 # If the batch size is too big we use gradient accumulation
 __UpperCAmelCase								=       1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  __UpperCAmelCase								=       batch_size // MAX_GPU_BATCH_SIZE
  __UpperCAmelCase								=       MAX_GPU_BATCH_SIZE

 set_seed(lowerCAmelCase      )
 __UpperCAmelCase	,      __UpperCAmelCase								=       get_dataloaders(lowerCAmelCase   ,	lowerCAmelCase      )
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 __UpperCAmelCase								=       AutoModelForSequenceClassification.from_pretrained('bert-base-cased'   ,	return_dict=lowerCAmelCase      )

 # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
 # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
 # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
 __UpperCAmelCase								=       model.to(accelerator.device      )

 # Instantiate optimizer
 __UpperCAmelCase								=       AdamW(params=model.parameters()   ,	lr=lowerCAmelCase      )

 # Instantiate scheduler
 __UpperCAmelCase								=       get_linear_schedule_with_warmup(
     optimizer=lowerCAmelCase   ,	num_warmup_steps=100   ,	num_training_steps=(len(lowerCAmelCase      ) * num_epochs) // gradient_accumulation_steps   ,	)

 # Prepare everything
 # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
 # prepare method.
 __UpperCAmelCase	,      __UpperCAmelCase	,      __UpperCAmelCase	,      __UpperCAmelCase	,      __UpperCAmelCase								=       accelerator.prepare(
     lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase      )

 # Now we train the model
 for epoch in range(lowerCAmelCase      ):
  model.train()
  for step, batch in enumerate(lowerCAmelCase      ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device      )
   __UpperCAmelCase								=       model(**lowerCAmelCase      )
   __UpperCAmelCase								=       outputs.loss
   __UpperCAmelCase								=       loss / gradient_accumulation_steps
   accelerator.backward(lowerCAmelCase      )
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

  model.eval()
  __UpperCAmelCase								=       0
  for step, batch in enumerate(lowerCAmelCase      ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device      )
   with torch.no_grad():
    __UpperCAmelCase								=       model(**lowerCAmelCase      )
   __UpperCAmelCase								=       outputs.logits.argmax(dim=-1      )
   __UpperCAmelCase	,      __UpperCAmelCase								=       accelerator.gather((predictions, batch['labels'])      )
   # New Code #
   # First we check if it's a distributed system
   if accelerator.use_distributed:
    # Then see if we're on the last batch of our eval dataloader
    if step == len(lowerCAmelCase      ) - 1:
     # Last batch needs to be truncated on distributed systems as it contains additional samples
     __UpperCAmelCase								=       predictions[: len(eval_dataloader.dataset      ) - samples_seen]
     __UpperCAmelCase								=       references[: len(eval_dataloader.dataset      ) - samples_seen]
    else:
     # Otherwise we add the number of samples seen
     samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
   metric.add_batch(
       predictions=lowerCAmelCase   ,	references=lowerCAmelCase   ,	)

  __UpperCAmelCase								=       metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}:"""   ,	lowerCAmelCase      )



def     __snake_case			(							):
 __UpperCAmelCase								=       argparse.ArgumentParser(description='Simple example of training script.'      )
 parser.add_argument(
     '--mixed_precision'   ,	type=lowerCAmelCase   ,	default=lowerCAmelCase   ,	choices=['no', 'fp16', 'bf16', 'fp8']   ,	help='Whether to use mixed precision. Choose'
     'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
     'and an Nvidia Ampere GPU.'   ,	)
 parser.add_argument('--cpu'   ,	action='store_true'   ,	help='If passed, will train on the CPU.'      )
 __UpperCAmelCase								=       parser.parse_args()
 __UpperCAmelCase								=       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
 training_function(lowerCAmelCase   ,	lowerCAmelCase      )


if __name__ == "__main__":
     main()




import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
		raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
		raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
__a    :	Any         =    logging.get_logger(__name__)

__a    :	Union[str, Any]         =    """Hello, World!"""
__a    :	Optional[int]         =    """en_XX"""





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				Path('''data_bin'''				)
				__lowercase					     =				FairseqXmodModel.from_pretrained(
				    model_name_or_path=str(Path(lowercase				).parent				)				,     checkpoint_file=Path(lowercase				).name				,     _name='''xmod_base'''				,     arch='''xmod_base'''				,     task='''multilingual_masked_lm'''				,     data_name_or_path=str(lowercase				)				,     bpe='''sentencepiece'''				,     sentencepiece_model=str(Path(lowercase				).parent / '''sentencepiece.bpe.model'''				)				,     src_dict=str(data_dir / '''dict.txt'''				)				,     )
				xmod.eval()  # disable dropout
				print(lowercase				)

				__lowercase					     =				xmod.model.encoder.sentence_encoder
				__lowercase					     =				XmodConfig(
				    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings				,     hidden_size=xmod.cfg.model.encoder_embed_dim				,     num_hidden_layers=xmod.cfg.model.encoder_layers				,     num_attention_heads=xmod.cfg.model.encoder_attention_heads				,     intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim				,     max_position_embeddings=514				,     type_vocab_size=1				,     layer_norm_eps=1E-5				,     pre_norm=xmod.cfg.model.encoder_normalize_before				,     adapter_reduction_factor=getattr(xmod.cfg.model				,     '''bottleneck'''				,     2				)				,     adapter_layer_norm=xmod.cfg.model.adapter_layer_norm				,     adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm				,     ln_before_adapter=xmod.cfg.model.ln_before_adapter				,     languages=xmod.cfg.model.languages				,     )
				if classification_head:
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

				print('''Our X-MOD config:'''				,     lowercase				)

				__lowercase					     =				XmodForSequenceClassification(lowercase				) if classification_head else XmodForMaskedLM(lowercase				)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				__lowercase					     =				xmod_sent_encoder.embed_tokens.weight
				__lowercase					     =				xmod_sent_encoder.embed_positions.weight
				__lowercase					     =				torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight				)  # just zero them out b/c xmod doesn't use them.

				__lowercase					     =				xmod_sent_encoder.layernorm_embedding.weight
				__lowercase					     =				xmod_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers				):
								# Encoder: start of layer
								__lowercase					     =				model.roberta.encoder.layer[i]
								__lowercase					     =				xmod_sent_encoder.layers[i]

								# self attention
								__lowercase					     =				layer.attention.self
								if not (
								    xmod_layer.self_attn.k_proj.weight.data.shape
								    == xmod_layer.self_attn.q_proj.weight.data.shape
								    == xmod_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size)				)
								):
												raise AssertionError('''Dimensions of self-attention weights do not match.'''				)

								__lowercase					     =				xmod_layer.self_attn.q_proj.weight
								__lowercase					     =				xmod_layer.self_attn.q_proj.bias
								__lowercase					     =				xmod_layer.self_attn.k_proj.weight
								__lowercase					     =				xmod_layer.self_attn.k_proj.bias
								__lowercase					     =				xmod_layer.self_attn.v_proj.weight
								__lowercase					     =				xmod_layer.self_attn.v_proj.bias

								# self-attention output
								__lowercase					     =				layer.attention.output
								if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
												raise AssertionError('''Dimensions of self-attention output weights do not match.'''				)
								__lowercase					     =				xmod_layer.self_attn.out_proj.weight
								__lowercase					     =				xmod_layer.self_attn.out_proj.bias
								__lowercase					     =				xmod_layer.self_attn_layer_norm.weight
								__lowercase					     =				xmod_layer.self_attn_layer_norm.bias

								# intermediate
								__lowercase					     =				layer.intermediate
								if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError('''Dimensions of intermediate weights do not match.'''				)
								__lowercase					     =				xmod_layer.fca.weight
								__lowercase					     =				xmod_layer.fca.bias

								# output
								__lowercase					     =				layer.output
								if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError('''Dimensions of feed-forward weights do not match.'''				)
								__lowercase					     =				xmod_layer.fca.weight
								__lowercase					     =				xmod_layer.fca.bias
								__lowercase					     =				xmod_layer.final_layer_norm.weight
								__lowercase					     =				xmod_layer.final_layer_norm.bias
								if bert_output.adapter_layer_norm is not None:
												__lowercase					     =				xmod_layer.adapter_layer_norm.weight
												__lowercase					     =				xmod_layer.adapter_layer_norm.bias

								if sorted(bert_output.adapter_modules.keys()				) != sorted(xmod_layer.adapter_modules.keys()				):
												raise AssertionError('''Lists of language adapters do not match.'''				)
								for lang_code, adapter in xmod_layer.adapter_modules.items():
												__lowercase					     =				bert_output.adapter_modules[lang_code]
												__lowercase					     =				xmod_layer.adapter_modules[lang_code]
												__lowercase					     =				from_adapter.fca.weight
												__lowercase					     =				from_adapter.fca.bias
												__lowercase					     =				from_adapter.fca.weight
												__lowercase					     =				from_adapter.fca.bias

								# end of layer

				if xmod_sent_encoder.layer_norm is not None:
								__lowercase					     =				xmod_sent_encoder.layer_norm.weight
								__lowercase					     =				xmod_sent_encoder.layer_norm.bias

				if classification_head:
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].dense.weight
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].dense.bias
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].out_proj.weight
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								__lowercase					     =				xmod.model.encoder.lm_head.dense.weight
								__lowercase					     =				xmod.model.encoder.lm_head.dense.bias
								__lowercase					     =				xmod.model.encoder.lm_head.layer_norm.weight
								__lowercase					     =				xmod.model.encoder.lm_head.layer_norm.bias
								__lowercase					     =				xmod.model.encoder.lm_head.weight
								__lowercase					     =				xmod.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				__lowercase					     =				xmod.encode(lowercase				).unsqueeze(0				)  # batch of size 1
				model.roberta.set_default_language(lowercase				)

				__lowercase					     =				model(lowercase				)[0]
				if classification_head:
								__lowercase					     =				xmod.model.classification_heads['''mnli'''](xmod.extract_features(lowercase				)				)
				else:
								__lowercase					     =				xmod.model(lowercase				,     lang_id=[SAMPLE_LANGUAGE]				)[0]
				print(our_output.shape				,     their_output.shape				)
				__lowercase					     =				torch.max(torch.abs(our_output - their_output				)				).item()
				print(F"max_absolute_diff = {max_absolute_diff}"				)  # ~ 1e-7
				__lowercase					     =				torch.allclose(lowercase				,     lowercase				,     atol=1E-3				)
				print('''Do both models output the same tensors?'''				,     '''🔥''' if success else '''💩'''				)
				if not success:
								raise Exception('''Something went wRoNg'''				)

				Path(lowercase				).mkdir(parents=lowercase				,     exist_ok=lowercase				)
				print(F"Saving model to {pytorch_dump_folder_path}"				)
				model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	List[str]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		parser.add_argument(
		    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
		)
		__a    :	List[str]         =    parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)